Pages that link to "Item:Q1744192"

The following pages link to The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems (Q1744192):

Displaying 50 items.

• Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks (Q5163210) (← links)
• Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions (Q5163229) (← links)
• SwitchNet: A Neural Network Model for Forward and Inverse Scattering Problems (Q5240806) (← links)
• Multilevel Fine-Tuning: Closing Generalization Gaps in Approximation of Solution Maps under a Limited Budget for Training Data (Q5857926) (← links)
• A Local Deep Learning Method for Solving High Order Partial Differential Equations (Q5864768) (← links)
• Predicting subdifferential switching surface in a steady-state complex heat transfer problem using deep learning (Q5883655) (← links)
• Stationary Density Estimation of Itô Diffusions Using Deep Learning (Q5886225) (← links)
• A Regularity Theory for Static Schrödinger Equations on \(\boldsymbol{\mathbb{R}}\)^<i>d</i> in Spectral Barron Spaces (Q5887733) (← links)
• Asymptotic-preserving schemes for multiscale physical problems (Q5887838) (← links)
• A Proof that Artificial Neural Networks Overcome the Curse of Dimensionality in the Numerical Approximation of Black–Scholes Partial Differential Equations (Q5889064) (← links)
• Learning-based local weighted least squares for algebraic multigrid method (Q6048416) (← links)
• JAX-DIPS: neural bootstrapping of finite discretization methods and application to elliptic problems with discontinuities (Q6048459) (← links)
• Neural Networks with Local Converging Inputs (NNLCI) for Solving Conservation Laws, Part I: 1D Problems (Q6049611) (← links)
• Neural networks based on power method and inverse power method for solving linear eigenvalue problems (Q6052345) (← links)
• Solving multiscale elliptic problems by sparse radial basis function neural networks (Q6054222) (← links)
• On a neural network approach for solving potential control problem of the semiclassical Schrödinger equation (Q6056185) (← links)
• De Rham compatible deep neural network FEM (Q6057971) (← links)
• Randomized neural network with Petrov-Galerkin methods for solving linear and nonlinear partial differential equations (Q6058946) (← links)
• An introduction to the mathematics of deep learning (Q6064555) (← links)
• Three ways to solve partial differential equations with neural networks — A review (Q6068232) (← links)
• Combining machine learning and domain decomposition methods for the solution of partial differential equations—A review (Q6068269) (← links)
• VC-PINN: variable coefficient physics-informed neural network for forward and inverse problems of PDEs with variable coefficient (Q6069931) (← links)
• Artificial neural networks for solving elliptic differential equations with boundary layer (Q6071007) (← links)
• Some elliptic second order problems and neural network solutions: existence and error estimates (Q6073185) (← links)
• A priori generalization error analysis of two-layer neural networks for solving high dimensional Schrödinger eigenvalue problems (Q6076649) (← links)
• A nonlocal energy‐informed neural network for isotropic elastic solids with cracks under thermomechanical loads (Q6082574) (← links)
• Phase-field DeepONet: physics-informed deep operator neural network for fast simulations of pattern formation governed by gradient flows of free-energy functionals (Q6084433) (← links)
• Solving nonconvex energy minimization problems in martensitic phase transitions with a mesh-free deep learning approach (Q6084532) (← links)
• Adaptive deep density approximation for fractional Fokker-Planck equations (Q6087826) (← links)
• The deep minimizing movement scheme (Q6087937) (← links)
• Physics-informed neural networks for approximating dynamic (hyperbolic) PDEs of second order in time: error analysis and algorithms (Q6087958) (← links)
• The Random Feature Method for Time-Dependent Problems (Q6090342) (← links)
• On the use of graph neural networks and shape‐function‐based gradient computation in the deep energy method (Q6092138) (← links)
• BINN: a deep learning approach for computational mechanics problems based on boundary integral equations (Q6094674) (← links)
• A cusp-capturing PINN for elliptic interface problems (Q6095097) (← links)
• Solving Elliptic Problems with Singular Sources Using Singularity Splitting Deep Ritz Method (Q6095431) (← links)
• Deep Ritz method with adaptive quadrature for linear elasticity (Q6096475) (← links)
• A New Certified Hierarchical and Adaptive RB-ML-ROM Surrogate Model for Parametrized PDEs (Q6097873) (← links)
• Exponential ReLU neural network approximation rates for point and edge singularities (Q6101269) (← links)
• Data-driven vortex solitons and parameter discovery of 2D generalized nonlinear Schrödinger equations with a \(\mathcal{PT}\)-symmetric optical lattice (Q6103701) (← links)
• The robust physics-informed neural networks for a typical fourth-order phase field model (Q6103706) (← links)
• Exponential Convergence of Deep Operator Networks for Elliptic Partial Differential Equations (Q6108133) (← links)
• Friedrichs Learning: Weak Solutions of Partial Differential Equations via Deep Learning (Q6108164) (← links)
• Deep learning discrete calculus (DLDC): a family of discrete numerical methods by universal approximation for STEM education to frontier research (Q6109277) (← links)
• A Chebyshev Polynomial Neural Network Solver for Boundary Value Problems of Elliptic Equations (Q6110098) (← links)
• A Hybrid Method for Three-Dimensional Semi-Linear Elliptic Equations (Q6110111) (← links)
• An Efficient Neural-Network and Finite-Difference Hybrid Method for Elliptic Interface Problems with Applications (Q6111297) (← links)
• Investigating and Mitigating Failure Modes in Physics-Informed Neural Networks (PINNs) (Q6111307) (← links)
• A Correction and Comments on “Multi-Scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains CiCP, 28(5):1970–2001,2020” (Q6111318) (← links)
• Finite element interpolated neural networks for solving forward and inverse problems (Q6118570) (← links)